OpenRaiser/Intern-Atlas huggingface dataset:
Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach
Venue: arXiv cs.CL 2020
Year: 2020

Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations. However, these approaches heavily rely on manual tagging of user reviews according to the predefined aspects as the input, a laborious and time-consuming process. Moreover, the underlying methods do not explain how and why the opposing aspect level polarities in a user review lead to the overall polarity. In this paper, we tackle these two problems by designing and implementing a new Multiple-Attention Network (MAN) approach for more powerful ABSA without the need for aspect tags using two new tag-free data sets crawled directly from TripAdvisor ({https://www.tripadvisor.com}). With the Self- and Position-Aware attention mechanism, MAN is capable of extracting both aspect level and overall sentiments from the text reviews using the aspect level and overall customer ratings, and it can also detect the vital aspect(s) leading to the overall sentiment polarity among different aspects via a new aspect ranking scheme. We carry out extensive experiments to demonstrate the strong performance of MAN compared to other state-of-the-art ABSA approaches and the explainability of our approach by visualizing and interpreting attention weights in case studies.
A Transactional Perspective on Execute-order-validate Blockchains
Venue: arXiv cs.DC 2020
Year: 2020

Smart contracts have enabled blockchain systems to evolve from simple cryptocurrency platforms, such as Bitcoin, to general transactional systems, such as Ethereum. Catering for emerging business requirements, a new architecture called execute-order-validate has been proposed in Hyperledger Fabric to support parallel transactions and improve the blockchain's throughput. However, this new architecture might render many invalid transactions when serializing them. This problem is further exaggerated as the block formation rate is inherently limited due to other factors beside data processing, such as cryptography and consensus. In this work, we propose a novel method to enhance the execute-order-validate architecture, by reducing invalid transactions to improve the throughput of blockchains. Our method is inspired by state-of-the-art optimistic concurrency control techniques in modern database systems. In contrast to existing blockchains that adopt database's preventive approaches which might abort serializable transactions, our method is theoretically more fine-grained. Specifically, unserializable transactions are aborted before ordering and the remaining transactions are guaranteed to be serializable. For evaluation, we implement our method in two blockchains respectively, FabricSharp on top of Hyperledger Fabric, and FastFabricSharp on top of FastFabric. We compare the performance of FabricSharp with vanilla Fabric and three related systems, two of which are respectively implemented with one standard and one state-of-the-art concurrency control techniques from databases. The results demonstrate that FabricSharp achieves 25% higher throughput compared to the other systems in nearly all experimental scenarios. Moreover, the FastFabricSharp's improvement over FastFabric is up to 66%.
Linguistically Driven Graph Capsule Network for Visual Question Reasoning
Venue: arXiv cs.CV 2020
Year: 2020

Recently, studies of visual question answering have explored various architectures of end-to-end networks and achieved promising results on both natural and synthetic datasets, which require explicitly compositional reasoning. However, it has been argued that these black-box approaches lack interpretability of results, and thus cannot perform well on generalization tasks due to overfitting the dataset bias. In this work, we aim to combine the benefits of both sides and overcome their limitations to achieve an end-to-end interpretable structural reasoning for general images without the requirement of layout annotations. Inspired by the property of a capsule network that can carve a tree structure inside a regular convolutional neural network (CNN), we propose a hierarchical compositional reasoning model called the "Linguistically driven Graph Capsule Network", where the compositional process is guided by the linguistic parse tree. Specifically, we bind each capsule in the lowest layer to bridge the linguistic embedding of a single word in the original question with visual evidence and then route them to the same capsule if they are siblings in the parse tree. This compositional process is achieved by performing inference on a linguistically driven conditional random field (CRF) and is performed across multiple graph capsule layers, which results in a compositional reasoning process inside a CNN. Experiments on the CLEVR dataset, CLEVR compositional generation test, and FigureQA dataset demonstrate the effectiveness and composition generalization ability of our end-to-end model.
Compressive Sensing Based Massive Access for IoT Relying on Media Modulation Aided Machine Type Communications
Venue: arXiv cs.IT 2020
Year: 2020

A fundamental challenge of the large-scale Internet-of-Things lies in how to support massive machine-type communications (mMTC). This letter proposes a media modulation based mMTC solution for increasing the throughput, where a massive multi-input multi-output based base station (BS) is used for enhancing the detection performance. For such a mMTC scenario, the reliable active device detection and data decoding pose a serious challenge. By leveraging the sparsity of the uplink access signals of mMTC received at the BS, a compressive sensing based massive access solution is proposed for tackling this challenge. Specifically, we propose a block sparsity adaptive matching pursuit algorithm for detecting the active devices, whereby the block-sparsity of the uplink access signals exhibited across the successive time slots and the structured sparsity of media modulated symbols are exploited for enhancing the detection performance. Moreover, a successive interference cancellation based structured subspace pursuit algorithm is conceived for data demodulation of the active devices, whereby the structured sparsity of media modulation based symbols found in each time slot is exploited for improving the detection performance. Finally, our simulation results verify the superiority of the proposed scheme over state-of-the-art solutions.
High-Performance Industrial Wireless: Achieving Reliable and Deterministic Connectivity over IEEE 802.11 WLANs
Venue: arXiv cs.NI 2020
Year: 2020

Communication for control-centric industrial applications is characterized by the requirements of very high reliability, very low and deterministic latency and high scalability. Typically, IEEE 802.11-based wireless local area networks (WLANs), also known as Wi-Fi networks, are deemed ineligible for industrial control applications owing to insufficient reliability and non-deterministic latency. This paper proposes a novel solution for providing reliable and deterministic communication, through Wi-Fi, in industrial environments. The proposed solution, termed as \textsf{HAR\(^\text{2}\)D-Fi} (\underline{H}ybrid channel \underline{A}ccess with \underline{R}edundancy for \underline{R}eliable and \underline{D}eterministic Wi-\underline{Fi}), adopts hybrid channel access mechanisms for achieving deterministic communication. It also provides temporal redundancy for enhanced reliability. \textsf{HAR\(^\text{2}\)D-Fi} implements different medium access control (MAC) designs that build on the standard physical (PHY) layer. Such designs can be classified into two categories: (a) MAC designs with pre-defined (physical) time-slotted schedule, and (b) MAC designs with virtual time-slotted schedule. Performance evaluation, based on analysis and system-level simulations, demonstrates the viability of \textsf{HAR\(^\text{2}\)D-Fi} for control-centric industrial applications.
Efficient sampling generation from explicit densities via Normalizing Flows
Venue: arXiv cs.LG 2020
Year: 2020

For many applications, such as computing the expected value of different magnitudes, sampling from a known probability density function, the target density, is crucial but challenging through the inverse transform. In these cases, rejection and importance sampling require suitable proposal densities, which can be evaluated and sampled from efficiently. We will present a method based on normalizing flows, proposing a solution for the common problem of exploding reverse Kullback-Leibler divergence due to the target density having values of 0 in regions of the flow transformation. The performance of the method will be demonstrated using a multi-mode complex density function.
Failure of Feasible Disjunction Property for $k$-DNF Resolution and NP-hardness of Automating It
Venue: arXiv cs.CC 2020
Year: 2020

We show that for every integer $k \geq 2$, the Res($k$) propositional proof system does not have the weak feasible disjunction property. Next, we generalize a recent result of Atserias and Müller [FOCS, 2019] to Res($k$). We show that if NP is not included in P (resp. QP, SUBEXP) then for every integer $k \geq 1$, Res($k$) is not automatable in polynomial (resp. quasi-polynomial, subexponential) time.
Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation
Venue: arXiv cs.LG 2020
Year: 2020

Large amounts of mobility data are being generated from many different sources, and several data mining methods have been proposed for this data. One of the most critical steps for trajectory data mining is segmentation. This task can be seen as a pre-processing step in which a trajectory is divided into several meaningful consecutive sub-sequences. This process is necessary because trajectory patterns may not hold in the entire trajectory but on trajectory parts. In this work, we propose a supervised trajectory segmentation algorithm, called Wise Sliding Window Segmentation (WS-II). It processes the trajectory coordinates to find behavioral changes in space and time, generating an error signal that is further used to train a binary classifier for segmenting trajectory data. This algorithm is flexible and can be used in different domains. We evaluate our method over three real datasets from different domains (meteorology, fishing, and individuals movements), and compare it with four other trajectory segmentation algorithms: OWS, GRASP-UTS, CB-SMoT, and SPD. We observed that the proposed algorithm achieves the highest performance for all datasets with statistically significant differences in terms of the harmonic mean of purity and coverage.
Sample-Specific Output Constraints for Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Neural networks reach state-of-the-art performance in a variety of learning tasks. However, a lack of understanding the decision making process yields to an appearance as black box. We address this and propose ConstraintNet, a neural network with the capability to constrain the output space in each forward pass via an additional input. The prediction of ConstraintNet is proven within the specified domain. This enables ConstraintNet to exclude unintended or even hazardous outputs explicitly whereas the final prediction is still learned from data. We focus on constraints in form of convex polytopes and show the generalization to further classes of constraints. ConstraintNet can be constructed easily by modifying existing neural network architectures. We highlight that ConstraintNet is end-to-end trainable with no overhead in the forward and backward pass. For illustration purposes, we model ConstraintNet by modifying a CNN and construct constraints for facial landmark prediction tasks. Furthermore, we demonstrate the application to a follow object controller for vehicles as a safety-critical application. We submitted an approach and system for the generation of safety-critical outputs of an entity based on ConstraintNet at the German Patent and Trademark Office with the official registration mark DE10 2019 119 739.
PathVQA: 30000+ Questions for Medical Visual Question Answering
Venue: arXiv cs.CL 2020
Year: 2020

Is it possible to develop an "AI Pathologist" to pass the board-certified examination of the American Board of Pathology? To achieve this goal, the first step is to create a visual question answering (VQA) dataset where the AI agent is presented with a pathology image together with a question and is asked to give the correct answer. Our work makes the first attempt to build such a dataset. Different from creating general-domain VQA datasets where the images are widely accessible and there are many crowdsourcing workers available and capable of generating question-answer pairs, developing a medical VQA dataset is much more challenging. First, due to privacy concerns, pathology images are usually not publicly available. Second, only well-trained pathologists can understand pathology images, but they barely have time to help create datasets for AI research. To address these challenges, we resort to pathology textbooks and online digital libraries. We develop a semi-automated pipeline to extract pathology images and captions from textbooks and generate question-answer pairs from captions using natural language processing. We collect 32,799 open-ended questions from 4,998 pathology images where each question is manually checked to ensure correctness. To our best knowledge, this is the first dataset for pathology VQA. Our dataset will be released publicly to promote research in medical VQA.
Adaptive Name Entity Recognition under Highly Unbalanced Data
Venue: arXiv cs.CL 2020
Year: 2020

For several purposes in Natural Language Processing (NLP), such as Information Extraction, Sentiment Analysis or Chatbot, Named Entity Recognition (NER) holds an important role as it helps to determine and categorize entities in text into predefined groups such as the names of persons, locations, quantities, organizations or percentages, etc. In this report, we present our experiments on a neural architecture composed of a Conditional Random Field (CRF) layer stacked on top of a Bi-directional LSTM (BI-LSTM) layer for solving NER tasks. Besides, we also employ a fusion input of embedding vectors (Glove, BERT), which are pre-trained on the huge corpus to boost the generalization capacity of the model. Unfortunately, due to the heavy unbalanced distribution cross-training data, both approaches just attained a bad performance on less training samples classes. To overcome this challenge, we introduce an add-on classification model to split sentences into two different sets: Weak and Strong classes and then designing a couple of Bi-LSTM-CRF models properly to optimize performance on each set. We evaluated our models on the test set and discovered that our method can improve performance for Weak classes significantly by using a very small data set (approximately 0.45\%) compared to the rest classes.
Modeling Contrary-to-Duty with CP-nets
Venue: arXiv cs.AI 2020
Year: 2020

In a ceteris-paribus semantics for deontic logic, a state of affairs where a larger set of prescriptions is respected is preferable to a state of affairs where some of them are violated. Conditional preference nets (CP-nets) are a compact formalism to express and analyse ceteris paribus preferences, which nice computational properties. This paper shows how deontic concepts can be captured through conditional preference models. A restricted deontic logic will be defined, and mapped into conditional preference nets. We shall also show how to model contrary to duties obligations in CP-nets and how to capture in this formalism the distinction between strong and weak permission.
Creating Synthetic Datasets via Evolution for Neural Program Synthesis
Venue: arXiv cs.LG 2020
Year: 2020

Program synthesis is the task of automatically generating a program consistent with a given specification. A natural way to specify programs is to provide examples of desired input-output behavior, and many current program synthesis approaches have achieved impressive results after training on randomly generated input-output examples. However, recent work has discovered that some of these approaches generalize poorly to data distributions different from that of the randomly generated examples. We show that this problem applies to other state-of-the-art approaches as well and that current methods to counteract this problem are insufficient. We then propose a new, adversarial approach to control the bias of synthetic data distributions and show that it outperforms current approaches.
AfricaOS: Using a distributed, proposal-based, replicated state machine towards liberation from the Berlin Conference of 1885
Venue: arXiv cs.GT 2020
Year: 2020

The Berlin Conference of 1885 has influenced the way native Africans, and the African Diaspora live their daily lives. France contractually controls several resources generated by the continent of Africa. Herein lies a technical proposal to free Africa from the financial and economic agreements coerced upon the continent over a century ago by utilizing decentralized collaboration through advanced technology. AfricaOS (AOS) aims to provide a philosophical, and fundamental framework for implementing a simple, distributed, collaborative computer for agreement amongst peers. The work also demonstrates an algebra over transactions, use of the protocol for privatization, a method for tokenizing barter economies, and methods to design mechanisms for use in implementing protocol behavior.
Challenges of Bridging the Gap between Mass People and Welfare Organizations in Bangladesh
Venue: arXiv cs.CY 2020
Year: 2020

Computing for the development of marginalized communities is a big deal of challenges for researchers. Different social organizations are working to develop the conditions of a specialized marginalized community namely Street Children, one of the most underprivileged communities in Bangladesh. However, lack of proper engagement among different social welfare organizations, donors, and the mass community limits the goal of the development of street children. Developing a virtual organization hub can eliminate communication gap as well as the information gap by involving people of all communities. However, some human imposed stigmas may often limit the rate of success of potential virtual computing solutions intended for organizations working with the marginalized communities, which we also face in our case. After a partial successful deployment, the design itself needs to be self comprehensive and trustworthy in order to overcome the stigmas that demand a reasonable amount of time. Moreover, after a wide scalable deployment, it is yet to be investigated whether the design of our computational solution can attain the goal for the facilitation of the organizations so that those organizations can become more effective for the development of street children than before.
Elle: Inferring Isolation Anomalies from Experimental Observations
Venue: arXiv cs.DB 2020
Year: 2020

Users who care about their data store it in databases, which (at least in principle) guarantee some form of transactional isolation. However, experience shows [Kleppmann 2019, Kingsbury and Patella 2019a] that many databases do not provide the isolation guarantees they claim. With the recent proliferation of new distributed databases, demand has grown for checkers that can, by generating client workloads and injecting faults, produce anomalies that witness a violation of a stated guarantee. An ideal checker would be sound (no false positives), efficient (polynomial in history length and concurrency), effective (finding violations in real databases), general (analyzing many patterns of transactions), and informative (justifying the presence of an anomaly with understandable counterexamples). Sadly, we are aware of no checkers that satisfy these goals. We present Elle: a novel checker which infers an Adya-style dependency graph between client-observed transactions. It does so by carefully selecting database objects and operations when generating histories, so as to ensure that the results of database reads reveal information about their version history. Elle can detect every anomaly in Adya et al's formalism [Adya et al. 2000] (except for predicates), discriminate between them, and provide concise explanations of each. This paper makes the following contributions: we present Elle, demonstrate its soundness, measure its efficiency against the current state of the art, and give evidence of its effectiveness via a case study of four real databases.
Resilient Distributed Diffusion in Networks with Adversaries
Venue: arXiv cs.MA 2020
Year: 2020

In this paper, we study resilient distributed diffusion for multi-task estimation in the presence of adversaries where networked agents must estimate distinct but correlated states of interest by processing streaming data. We show that in general diffusion strategies are not resilient to malicious agents that do not adhere to the diffusion-based information processing rules. In particular, by exploiting the adaptive weights used for diffusing information, we develop time-dependent attack models that drive normal agents to converge to states selected by the attacker. We show that an attacker that has complete knowledge of the system can always drive its targeted agents to its desired estimates. Moreover, an attacker that does not have complete knowledge of the system including streaming data of targeted agents or the parameters they use in diffusion algorithms, can still be successful in deploying an attack by approximating the needed information. The attack models can be used for both stationary and non-stationary state estimation.In addition, we present and analyze a resilient distributed diffusion algorithm that is resilient to any data falsification attack in which the number of compromised agents in the local neighborhood of a normal agent is bounded. The proposed algorithm guarantees that all normal agents converge to their true target states if appropriate parameters are selected. We also analyze trade-off between the resilience of distributed diffusion and its performance in terms of steady-state mean-square-deviation (MSD) from the correct estimates. Finally, we evaluate the proposed attack models and resilient distributed diffusion algorithm using stationary and non-stationary multi-target localization.
Learning End-to-End Codes for the BPSK-constrained Gaussian Wiretap Channel
Venue: arXiv cs.LG 2020
Year: 2020

Finite-length codes are learned for the Gaussian wiretap channel in an end-to-end manner assuming that the communication parties are equipped with deep neural networks (DNNs), and communicate through binary phase-shift keying (BPSK) modulation scheme. The goal is to find codes via DNNs which allow a pair of transmitter and receiver to communicate reliably and securely in the presence of an adversary aiming at decoding the secret messages. Following the information-theoretic secrecy principles, the security is evaluated in terms of mutual information utilizing a deep learning tool called MINE (mutual information neural estimation). System performance is evaluated for different DNN architectures, designed based on the existing secure coding schemes, at the transmitter. Numerical results demonstrate that the legitimate parties can indeed establish a secure transmission in this setting as the learned codes achieve points on almost the boundary of the equivocation region.